This approach can be beneficial in many machine learning tasks including object detection, whose final result is a list of bounding boxes (bboxes) and the corresponding classes.
In this task, the network is devoted to finding the position of the localized objects in an image, as well as identifying which class they belong to.
Hereinafter, we will refer to this specific application as Semi-Supervised Object Detection (SSOD). In [1]}, authors have collected many interesting ideas applied to SSOD.
Among them, the one that seems more promising is the Mean Teacher [2]}, which uses the Exponential Moving Average (EMA) as a knowledge transfer technique from the Student to the Teacher.
In particular, while the Teacher produces pseudo-labels on unlabeled and weakly augmented images to obtain more reliable labels, the Student is trained using the pseudo-labels as ground-truth on the same images, but strongly augmented to differentiate the training.
In weak augmentation, only a random horizontal flip is applied, while in strong augmentation, other transformations such as randomly adding color jittering, grayscale, Gaussian blur, and cutout patches are also used.
